We evaluate our method on three well-known model architectures:, i.e., SSD [ Named Entity Recognition, and Question Answering. Find more details in Table 5. Recall, ROC-AUC, and Average Scanning Overheads for each model. A value of 1 indicates perfect classification, while a value of 0.5 indicates To the best of our knowledge, there is no existing detection methods for object detection models. We evaluate the IoU threshold used to calculate the ASR of inverted triggers. However, a threshold of 0.7 tends to degrade the Different score thresholds are tested when computing the ASR of inverted triggers.

It suffers from performance degradation as the training goes on.

Some previous papers (e.g., MIM [7])claimedthat ensemble with weighted logits (equation (4) in main text) outperforms ensemble with weighted probabilities and weighted combination of loss (equations(3)and(5)in main text). In our experiments, shown in Figure 6b, we observe that weighted combination of surrogate loss functions provide similar or even higher fooling rate compared to weighted probabilities or logits.

The rapid progress of Deepfake technology has made face swapping highly realistic, raising concerns about the malicious use of fabricated facial content. Existing methods often struggle to generalize to unseen domains due to the diverse nature of facial manipulations. In this paper, we revisit the generation process and identify a universal principle: Deepfake images inherently contain information from both source and target identities, while genuine faces maintain a consistent identity. Building upon this insight, we introduce DiffusionFake, a novel plug-and-play framework that reverses the generative process of face forgeries to enhance the generalization of detection models. DiffusionFake achieves this by injecting the features extracted by the detection model into a frozen pre-trained Stable Diffusion model, compelling it to reconstruct the corresponding target and source images. This guided reconstruction process constrains the detection network to capture the source and target related features to facilitate the reconstruction, thereby learning rich and disentangled representations that are more resilient to unseen forgeries. Extensive experiments demonstrate that DiffusionFake significantly improves cross-domain generalization of various detector architectures without introducing additional parameters during inference.

Inspired by recent non-maximum suppression-free 2D object detection models, we propose a 3D object detection architecture on point clouds. Our method models 3D object detection as message passing on a dynamic graph, generalizing the DGCNN framework to predict a set of objects. In our construction, we remove the necessity of post-processing via object confidence aggregation or non-maximum suppression. To facilitate object detection from sparse point clouds, we also propose a set-to-set distillation approach customized to 3D detection.